Skip to main content
SpringerLink
Log in

A Software Tool for Lyophilization Primary Drying Process Development and Scale-up Including Process Heterogeneity, I: Laboratory-Scale Model Testing

  • Research Article
  • Theme: Modeling and Simulations of Drug Product Manufacturing Unit Operations
  • Published:
AAPS PharmSciTech Aims and scope Submit manuscript

Abstract

Freeze-drying is a deceptively complex operation requiring sophisticated design of a robust and efficient process that includes understanding and planning for heterogeneity across the batch and shifts in parameters due to vial or lyophilizer changes. A software tool has been designed to assist in process development and scale-up based on a model that includes consideration of the process heterogeneity. Two drug formulations were used to test the ability of the new tool to develop a freeze-drying cycle and correctly predict product temperatures and drying times. Model inputs were determined experimentally, and the primary drying heterogeneous freeze-drying model was used to design drying cycles that provided data to verify the accuracy of model-predicted product temperature and primary drying time. When model inputs were accurate, model-predicted primary drying times were within 0.1 to 15.9% of experimentally measured values, and product temperature accuracy was between 0.2 and 1.2°C for three vial locations, center, inner edge, and outer edge. However, for some drying cycles, differences in vial heat transfer coefficients due to changes in shelf and product temperature as well as altered product resistance due to product temperature-dependent microcollapse increased inaccuracy (up to 28.6% difference in primary drying time and 5.1°C difference in product temperature). This highlights the need for careful determination of experimental conditions used to calculate model inputs. In future efforts, full characterization of location- and shelf temperature-dependentKv as well as location- and product temperature-dependentRp will enhance the accuracy of the predictions by the model within the user-friendly software.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. SEQENS. What is driving the growing demand for lyophilization? Available from: https://cdmo.seqens.com/api-manufacturing/what-is-driving-the-growing-demand-for-lyophilization/, [Accessed 24th April 2020].

  2. Chang BS, Reilly M, Chang H. Lyophilized biologics. In: Varshney D, Singh M, editors. Lyophilized biologics and vaccines: modality-based approaches. New York: Springer; 2015.

    Google Scholar 

  3. Pikal MJ. Freeze drying. In: Encyclopedia of pharmaceutical technology, vol. 1299. N Y: Marcel Dekker; 2002. p. 1326.

    Google Scholar 

  4. Mirasol F. Lyophilization presents complex challenges. BioPharm Int. 2020;33(1).

  5. Chang BS, Fischer N. Development of an efficient single-step freeze-drying cycle for protein formulations. Pharm Res. 1995;12(6):831–7.

    Article  CAS  Google Scholar 

  6. Mockus LN, Paul TW, Pease NA, Harper NJ, Basu PK, Oslos EA, Sacha GA, Kuu WY, Hardwick LM, Karty JJ, Pikal MJ, Hee E, Khan MA, Nail SL. Quality by design in formulation and process development for a freeze-dried, small molecule parenteral product: a case study. Pharm Dev Technol. 2011;16(6):549–76.

    Article  CAS  Google Scholar 

  7. Patel SM, Pikal MJ. Lyophilization process design space. J Pharm Sci. 2013;102(11):3883–7.

    Article  CAS  Google Scholar 

  8. Roy ML, Pikal MJ. Process control in freeze drying: determination of the end point of sublimation drying by an electronic moisture sensor. J Parenter Sci Technol. 1989;43:60–6.

    CAS  PubMed  Google Scholar 

  9. Tang X, Pikal MJ. Design of freeze-drying processes for pharmaceuticals: practical advice. Pharm Res. 2004;21(2):191–200.

    Article  CAS  Google Scholar 

  10. Schneid SC, Gieseler H, Kessler WJ, Luthra SA, Pikal MJ. Optimization of the secondary drying step in freeze drying using TDLAS technology. PharmSciTech. 2011;12(1):379–87.

    Article  Google Scholar 

  11. Rambhatla S, Tchessalov S, Pikal MJ. Heat and mass transfer scale-up issues during freeze-drying, III: control and characterization of dryer differences via operational qualification tests. PharmSciTech. 2006;7(2):E61–70.

    Article  Google Scholar 

  12. MacKenzie A. Collpase during freeze drying – qualitative and quantitative aspects. In: Goldblith S, Rey L, Rothmayr W, editors. Freeze drying and advanced food technology. London: Academic Press; 1975.

    Google Scholar 

  13. Pikal MJ, Shah S. The collapse temperature in freeze-drying - dependence on measurement methodology and rate of water removal from the glassy phase. Int J Pharm. 1990;62(2–3):165–86.

    Article  CAS  Google Scholar 

  14. Greco K, Mujat M, Galbally-Kinney KL, Hammer DX, Ferguson RD, Iftimia N, Mulhall P, Sharma P, Kessler WJ, Pika MJ. Accurate prediction of collapse temperature using optical coherence tomography-based freeze-drying microscopy. J Pharm Sci. 2013;102(6):1773–85.

    Article  CAS  Google Scholar 

  15. Depaz RA, Pansare S, Patel SM. Freeze-drying above the glass transition temperature in amorphous protein formulations while maintaining product quality and improving process efficiency. J Pharm Sci. 2016;105(1):40–9.

    Article  CAS  Google Scholar 

  16. Johnson R, Lewis L. Freeze-drying protein formulations above their collapse temperatures: possible issues and concerns. Am Pharm Rev. 2011; Available from https://www.americanpharmaceuticalreview.com/Featured-Articles/37021-Freeze-Drying-Protein-Formulations-above-their-Collapse-Temperatures-Possible-Issues-and-Concerns/ [Accessed 29th November 2020].

  17. Pikal MJ. Freeze-drying of proteins. Part I: Process Design. Biochem Pharmacol. 1990;3:18–28.

    CAS  Google Scholar 

  18. Pikal MJ. Use of laboratory data in freeze drying process design: heat and mass transfer coefficients and the computer simulation of freeze drying. J Parenter Sci Technol. 1985;39:115–38.

    CAS  PubMed  Google Scholar 

  19. Scutella B, Bourles E. Development of a freeze-drying cycle via design space approach: a case study on vaccines. Pharm Dev Technol. 2020;25(10):1302–13.

    Article  CAS  Google Scholar 

  20. Pisano R, Fissore D, Barresi AA. Freeze-drying cycle optimization using model predictive control techniques. Ind Eng Chem Res. 2011;50(12):7363–79.

    Article  CAS  Google Scholar 

  21. Shivkumar G, Kazarin PS, Strongrich AD, Alexeenko AA. LyoPRONTO: an open-source lyophilization process optimization tool. PharmSciTech. 2019;20:238.

    Google Scholar 

  22. Wegiel L. Primary drying optimization using a three-dimensional design space. Presented at CPPR Conference Freeze Drying of Pharmaceuticals and Biologicals Conference. Germany: Garmisch-Partenkirchen; 2014. p. 23–6.

    Google Scholar 

  23. Nail SL, Searles JM. Elements of quality by design in development and scale-up of freeze-dried parenterals. BioPharm Int. 2008;21(1):44–52.

    CAS  Google Scholar 

  24. Patel AM, Chaudhuri S, Pikal MJ. Choked flow and importance of Mach I in freeze-drying process design. Chem Eng Sci. 2010;65(21):5716–27.

    Article  CAS  Google Scholar 

  25. Pikal MJ, Pande P, Bogner R, Sane P, Mudhivarthi V, Sharma P. Impact of natural variations in freeze-drying parameters on product temperature history: application of quasi steady-state heat and mass transfer and simple statistics. PharmSciTech. 2018;19(7):2828–42.

    Article  Google Scholar 

  26. Pikal MJ, Bogner R, Mudhivarthi V, Sharma P, Sane P. Freeze-drying process development and scale-up: scale-up of edge vial versus center vial heat transfer coefficients, Kv. J Pharm Sci. 2016;105(11):3333–43.

    Article  CAS  Google Scholar 

  27. Gieseler H, Kessler WJ, Finson MF, Davis SJ, Mulhall PA, Bons V, Debo DJ, Pikal MJ. Evaluation of tunable diode laser absorption spectroscopy for in-process water vapor mass flux measurements during freeze-drying. J Pharm Sci. 2007;96(7):1776–93.

    Article  CAS  Google Scholar 

  28. Schneid S, Gieseler H, Kessler W, Pikal MJ. Position dependent vial heat transfer coefficient: a comparison of tunable diode laser absorption spectroscopy and gravimetric measurements. Presented at CPPR Freeze-Drying of Pharmaceuticals and Biologicals. Germany: Garmisch-Partenkirchen; 2006. p. 3–6.

    Google Scholar 

  29. Kuu WY, Nail SL, Sacha G. Rapid determination of vial heat transfer parameters using tunable diode laser absorption spectroscopy (TDLAS) in response to step-changes in pressure set-point during freeze-drying. J Pharm Sci. 2009;98(3):1136–54.

    Article  CAS  Google Scholar 

  30. Schneid SC, Gieseler H, Kessler WJ, Pikal MJ. Non-invasive product temperature determination during primary drying using tunable diode laser absorption spectroscopy. J Pharm Sci. 2009;98(9):3401–18.

    Article  Google Scholar 

  31. Kuu WY, O’Bryan KR, Hardwick L, Paul TW. Product mass transfer resistance directly determined during freeze-drying cycle runs using tunable diode laser absorption spectroscopy (TDLAS) and pore diffusion model. Pharm Dev Technol. 2011;16(4):343–57.

    Article  CAS  Google Scholar 

  32. Bogner R. Factors that influence product resistance and methods to measure product resistance. SP Scientific Webinar, 2020 March 23rd. Available from: https://www.spscientific.com/Webinars/Archives / [Accessed 10th October 2020].

  33. Sharma P, Kessler WJ, Bogner R, Thakur M, Pikal MJ. Applications of the tunable diode laser absorption spectroscopy: in-process estimation of primary drying heterogeneity and product temperature during lyophilization. J Pharm Sci. 2019;108(1):416–30.

    Article  CAS  Google Scholar 

  34. Tang X, Nail SL, Pikal MJ. Freeze-drying process design by manometric temperature measurements: design of a smart freeze-dryer. Pharm Res. 2005;22(4):685–700.

    Article  CAS  Google Scholar 

  35. Pikal MJ, Roy ML, Shah S. Mass and heat transfer in vial freeze-drying of pharmaceuticals: role of the vial. J Pharm Sci. 1984;73(9):1224–37.

    Article  CAS  Google Scholar 

  36. Engineering ToolBox, (2004). Ice - thermal properties. [online] Available at: https://www.engineeringtoolbox.com/ice-thermal-properties-d_576.html [Accessed 27 July 2021].

  37. Jancso G, Pupezin J, Van Hook WA. Vapor pressure of ice between+ 10-2 and-1020. J Phys Chem. 1970;74(15):2984–9.

    Article  CAS  Google Scholar 

  38. Patel SM, Nail SL, Pikal MJ, Geidobler R, Winter G, Hawe A, Davagnino J, Gupta SR. Lyophilized drug product cake appearance: what is acceptable? JPharmSci. 2017;106:1706–21.

    CAS  Google Scholar 

Download references

Funding

This work was performed under a Project Award Agreement from the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) and financial assistance award 70NANB17H002 from the US Department of Commerce, National Institute of Standards and Technology. Drug substance was provided at no cost by Genentech and Merck.

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, USA

    Robin Bogner & Arushi Manchanda

  2. Physical Sciences Inc., 20 New England Business Center, Andover, MA, USA

    Emily Gong, William Kessler & Michael Hinds

  3. Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, USA

    Seongkyu Yoon, Huolong Liu & Richard Marx

  4. Genentech, South San Francisco, California, USA

    Jessie Zhao & Puneet Sharma

  5. Merck, West Point, PA, USA

    Akhilesh Bhambhani & Justin Stanbro

  6. Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA

    Alina Alexeenko & Petr Kazarin

Authors
  1. Robin Bogner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emily Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. William Kessler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Hinds
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arushi Manchanda
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Seongkyu Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Huolong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Richard Marx
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jessie Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Puneet Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Akhilesh Bhambhani
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Justin Stanbro
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Alina Alexeenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Petr Kazarin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Robin Bogner, Emily Gong, William Kessler, and Arushi Manchanda. Michael Hinds developed the software. Jessie Zhao, Puneet Sharma, Akhilesh Bhambhani, and Justin Stanbro provided drug substance. The first draft of the manuscript was written by Robin Bogner, Emily Gong, and William Kessler, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Emily Gong.

Ethics declarations

Conflict of Interest

The authors declare no competing interests

Additional information

Guest Editors: Alexander Russell and Maxx Capece

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplemental Figure 1

Lyophilization primary drying process modeling software Graphical User Interface used to fit product resistance data and determine product resistance parameters R0, A1 and A2. (PNG 7365 kb)

High Resolution Image (TIF 991 kb)

Supplemental Figure 2

Product resistance to drying, Rp, at Ldry = 2/3 Lmax as a function of the average product temperature during primary drying for Formulation B. (PNG 4040 kb)

High Resolution Image (TIF 47 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bogner, R., Gong, E., Kessler, W. et al. A Software Tool for Lyophilization Primary Drying Process Development and Scale-up Including Process Heterogeneity, I: Laboratory-Scale Model Testing. AAPS PharmSciTech 22, 274 (2021). https://doi.org/10.1208/s12249-021-02134-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1208/s12249-021-02134-3

KEY WORDS

Search

Navigation